Example usage for edu.stanford.nlp.util Index size

Introduction

In this page you can find the example usage for edu.stanford.nlp.util Index size.

Prototype

int size();

Source Link

Document

Returns the number of indexed objects.

Usage

From source file:conditionalCFG.ConditionalCFGParser.java

License:Open Source License

public ConditionalCFGParser(BinaryGrammar bg, UnaryGrammar ug, ConditionalLexicon lex, Options op,
        Index<String> stateIndex, Index<String> wordIndex, Index<String> tagIndex) {
    //    System.out.println("ExhaustivePCFGParser constructor called.");
    this.bg = bg;
    this.ug = ug;
    this.lex = lex;
    this.op = op;
    this.tlp = op.langpack();
    goalStr = tlp.startSymbol();//  w w  w  .  j  a  v a 2s. c om
    this.stateIndex = stateIndex;
    this.wordIndex = wordIndex;
    this.tagIndex = tagIndex;
    tf = new LabeledScoredTreeFactory();

    numStates = stateIndex.size();
    isTag = new boolean[numStates];
    // tag index is smaller, so we fill by iterating over the tag index
    // rather than over the state index
    for (String tag : tagIndex.objectsList()) {
        int state = stateIndex.indexOf(tag);
        if (state < 0) {
            continue;
        }
        isTag[state] = true;
    }
}

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From source file:gr.aueb.cs.nlp.wordtagger.classifier.SVMWindows64Factory.java

License:Open Source License

/**
 * Converts the svm_struct weight Counter (in which the weight for a feature/label pair
 * correspondes to ((labelIndex * numFeatures)+(featureIndex+1))) into a weight Counter
 * using the actual features and labels.
 *//*from w  w w .j av a 2s  .  c o m*/
private ClassicCounter<Pair<F, L>> convertSVMStructWeights(ClassicCounter<Integer> weights,
        Index<F> featureIndex, Index<L> labelIndex) {
    // int numLabels = labelIndex.size();
    int numFeatures = featureIndex.size();
    ClassicCounter<Pair<F, L>> newWeights = new ClassicCounter<Pair<F, L>>();
    for (int i : weights.keySet()) {
        L l = labelIndex.get((i - 1) / numFeatures); // integer division on purpose
        F f = featureIndex.get((i - 1) % numFeatures);
        double w = weights.getCount(i);
        newWeights.incrementCount(new Pair<F, L>(f, l), w);
    }

    return newWeights;
}

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